In a virtual colonography (VC) the patient undergoes a CT (computed tomography) scan, and the data are used to create a 3D or 2D representation of the colon. VC aims to quantify the internal texture of the colon. Common VC visualization techniques include the virtual Fly-Through (FT) and virtual Fly-Over (FO). Both simulate a real colonoscopy by moving a virtual camera with a specific field of view along a planned path inside the colon, rendering internal views.
An essential aspect of any computer-aided diagnosis colonography system is a means to have accurate segmentation of the colon. Colon segmentation is a challenging problem because the colon is highly topologically variable, it is an asymmetrically askew organ (i.e. Haustral folds), and Hounsfield intensity regions consistent with air, soft tissue and high-attenuation structures define the various regions of the colon. The presence of residual stool, lesions, and disconnected colon segments further add to the difficulties of virtual visualization.
In the academic literature both semi-automated and fully-automated colon segmentation algorithms have been proposed. In general, automated approaches use a combination of region growing and tissue classification. The prior art teaches region growing based on gradient magnitude and distance transforms and deformable geometric models. These techniques can be inaccurate, complex and expensive. Tissue classification methods include simple thresholding and principle component analysis.
Knowledge-based or anatomy-based colon segmentation algorithms have also been used. One two-step method utilizes region growing to extract extra segmented regions, such as the small bowel and stomach, in conjunction with an “anatomy-based extraction” that removes outer-air, bone and lung regions to enhance initial segmentation results. Lu et al. proposed a two-tiered approach that consists of a pre-segmentation step that classifies regions in the abdominal cavity as colon or extra-colonic (i.e. stomach, small bowel, etc.) using statistical modeling on geometry based features. The output is evaluated using a colon tracking algorithm, “daisy-chaining”, integrated with distance and geometry statistics per patient to handle moderately or poorly distended colon regions. However, these methods are not highly effective for segmenting the colon.
Thus, a method is needed that will provide more accurate segmentation of the colon or other irregular shaped structure. The present development aid in virtual colonography by creating a more defined and complete representation of the organ. The approach requires minimal memory and computational time while preserving VCs benefits for clinicians and patients.